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A Meta-Learning Sch...
A Meta-Learning Scheme for Adaptive Short-Term Network Traffic Prediction
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- He, Qing, 1979- (författare)
- KTH,Nätverk och systemteknik
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- Moayyedi, Arash (författare)
- KTH,Nätverk och systemteknik
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- Dán, György (författare)
- KTH,Nätverk och systemteknik
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Koudouridis, Georgios P. (författare)
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Tengkvist, Per (författare)
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(creator_code:org_t)
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020
- 2020
- Engelska.
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Ingår i: IEEE Journal on Selected Areas in Communications. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 0733-8716 .- 1558-0008. ; 38:10, s. 2271-2283
- Relaterad länk:
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Network traffic prediction is a fundamental prerequisite for dynamic resource provisioning in wireline and wireless networks, but is known to be challenging due to non-stationarity and due to its burstiness and self-similar nature. The prediction of network traffic at the user level is particularly challenging, because the traffic characteristics emerge from a complex interaction of user level and application protocol behavior. In this work we address the problem of predicting the network traffic at the user level over a short horizon, motivated by its applications in cellular scheduling. Motivated by recent works on robust adversarial learning, we treat the prediction problem for non-stationary traffic in an adversarial context, and propose a meta-learning scheme that consists of a set of predictors, each optimized to predict a particular kind of traffic, and of a master policy that is trained for choosing the best fit predictor dynamically based on recent prediction performance, using deep reinforcement learning. We evaluate the proposed meta-learning scheme on a variety of traffic traces consisting of video and non-video traffic. Our results show that it consistently outperforms state-of-the-art predictors, and can adapt to before unseen traffic without the need for retraining the individual predictors.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Predictive models
- Time series analysis
- Streaming media
- Mathematical model
- Wireless networks
- Aggregates
- Downlink
- Meta learning
- deep reinforcement learning
- network traffic prediction
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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